Nck adaptor proteins modulate differentiation and effector function of T cells
Bibliographic record
Abstract
Understanding the molecular mechanisms regulating T cell reactivity is required for successful reprogramming of immune responses in medical conditions, characterized by dysfunctions of the immune system. Nck proteins are cytoplasmic adaptors mediating diverse cellular functions, including TCR signaling. By enhancing TCR signal strength, Nck proteins influence thymic selection and regulate the size and sensitivity of the peripheral T cell repertoire. Here, we investigated the contribution of Nck proteins to CD4(+) T cell differentiation and effector function using Nck.T(-/-) mice. Impaired GC formation and reduced Tfh were observed in Nck.T(-/-) mice after immunization with T cell-dependent antigens. Th2/Tfh-related cytokines, such as IL-4, IL-10, and IL-21, were decreased in Nck.T(-/-) mice T cells. Moreover, an increased susceptibility to cell death of Tfh cells in Nck.T(-/-) mice was associated with decreased levels of Akt phosphorylation. As a result of this dysregulation in Tfh cells of Nck.T(-/-) mice, we found impaired production and affinity maturation of antibodies against T cell-dependent antigens. Thus, Nck proteins not only participate in thymic selection and generation of the peripheral T cell repertoire but also are involved in the differentiation and effector functions of CD4(+) T cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".